Adaptive scale adjustment design of unsharp masking filters for image contrast enhancement

Kwok, N; Shi, H; Fang, G; Ha, Q

Adaptive scale adjustment design of unsharp masking filters for image contrast enhancement

Kwok, N Shi, H Fang, G Ha, Q

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Machine Learning and Cybernetics, 2013, 2 pp. 884 - 889
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Kwok, N	en_US
dc.contributor.author	Shi, H	en_US
dc.contributor.author	Fang, G	en_US
dc.contributor.author	Ha, Q https://orcid.org/0000-0003-0978-1758	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	Proceedings - International Conference on Machine Learning and Cybernetics, 2013, 2 pp. 884 - 889	en_US
dc.identifier.isbn	9781479902576	en_US
dc.identifier.issn	2160-133X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30098
dc.description.abstract	© 2013 IEEE. The unsharp masking filter (UMF) has been widely used in image processing front ends for contrast enhancement. The filter, being easy to implement, is based on the concept of augmenting a scaled and high-passed version of the image to itself. The UMF performance is critically dependent on the generation of the highpassed signal to be added as well as its associated scale factor. However, the optimal choice of filter parameters still remains a challenging task due to possible intensity clipping problems where the filtered pixel magnitude is vulnerable to be out of the permitted display ranges. In this research, an adaptive scheme is formulated such that the scale is derived from the pixel intensity of the input image. Specifically, pixels in the mid-range intensity will be assigned a larger scaling factor according to a Gaussian-like profile. In addition, the optimal profile coefficients and the width of the high-pass generator window are determined by adopting the particle swarm optimization algorithm. Satisfactory simulation results obtained from a collection of a large set of images have shown the effectiveness of the proposed image contrast enhancement approach.	en_US
dc.relation.ispartof	Proceedings - International Conference on Machine Learning and Cybernetics	en_US
dc.relation.isbasedon	10.1109/ICMLC.2013.6890408	en_US
dc.title	Adaptive scale adjustment design of unsharp masking filters for image contrast enhancement	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Tianjin, China
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2	en_US

Abstract:

© 2013 IEEE. The unsharp masking filter (UMF) has been widely used in image processing front ends for contrast enhancement. The filter, being easy to implement, is based on the concept of augmenting a scaled and high-passed version of the image to itself. The UMF performance is critically dependent on the generation of the highpassed signal to be added as well as its associated scale factor. However, the optimal choice of filter parameters still remains a challenging task due to possible intensity clipping problems where the filtered pixel magnitude is vulnerable to be out of the permitted display ranges. In this research, an adaptive scheme is formulated such that the scale is derived from the pixel intensity of the input image. Specifically, pixels in the mid-range intensity will be assigned a larger scaling factor according to a Gaussian-like profile. In addition, the optimal profile coefficients and the width of the high-pass generator window are determined by adopting the particle swarm optimization algorithm. Satisfactory simulation results obtained from a collection of a large set of images have shown the effectiveness of the proposed image contrast enhancement approach.

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http://hdl.handle.net/10453/30098